Datasets:
ArXiv:
License:
| { | |
| "name": "46_Speech_Recognition_DeepSpeech_LibriSpeech_DL", | |
| "query": "I'd like to develop a speech recognition system using the DeepSpeech library and the LibriSpeech dataset for me. Could you implement data loading and audio preprocessing, including noise reduction and normalization, in `src/data_loader.py`? Tune the hyperparameters such as learning rate and batch size in `src/train.py`. Please save the recognition results in `results/recognition_results.txt`. Next, create visualizations of the audio processing stages (like waveform and spectrogram) and save them as `results/figures/audio_visualization.png`. Generate a detailed report on recognition accuracy, error analysis, and suggestions for future improvements, and save it as `results/recognition_report.md`. Additionally, document the setup process for DeepSpeech, with tips for common installation issues, with [DeepSpeech documentation](https://deepspeech.readthedocs.io/en/r0.9/) as a reference. Save the final model in `models/saved_models/`. Thanks in advance!", | |
| "tags": [ | |
| "Audio Processing" | |
| ], | |
| "requirements": [ | |
| { | |
| "requirement_id": 0, | |
| "prerequisites": [], | |
| "criteria": "\"LibriSpeech\" dataset is loaded in `src/data_loader.py`.", | |
| "category": "Dataset or Environment", | |
| "satisfied": null | |
| }, | |
| { | |
| "requirement_id": 1, | |
| "prerequisites": [ | |
| 0 | |
| ], | |
| "criteria": "Audio preprocessing, including noise reduction and normalization, is performed in `src/data_loader.py`.", | |
| "category": "Data preprocessing and postprocessing", | |
| "satisfied": null | |
| }, | |
| { | |
| "requirement_id": 2, | |
| "prerequisites": [ | |
| 1 | |
| ], | |
| "criteria": "Hyperparameters such as learning rate and batch size are tuned in `src/train.py`.", | |
| "category": "Machine Learning Method", | |
| "satisfied": null | |
| }, | |
| { | |
| "requirement_id": 3, | |
| "prerequisites": [ | |
| 2 | |
| ], | |
| "criteria": "Save the speech recognition model in `models/saved_models/`.", | |
| "category": "Save Trained Model", | |
| "satisfied": null | |
| }, | |
| { | |
| "requirement_id": 4, | |
| "prerequisites": [ | |
| 2 | |
| ], | |
| "criteria": "Recognition results are saved as `results/recognition_results.txt`.", | |
| "category": "Other", | |
| "satisfied": null | |
| }, | |
| { | |
| "requirement_id": 5, | |
| "prerequisites": [ | |
| 1 | |
| ], | |
| "criteria": "Visualizations of audio processing, like waveform and spectrogram, are generated and saved as `results/figures/audio_visualization.png`.", | |
| "category": "Visualization", | |
| "satisfied": null | |
| }, | |
| { | |
| "requirement_id": 6, | |
| "prerequisites": [ | |
| 2 | |
| ], | |
| "criteria": "A report containing recognition accuracy, error analysis, and future improvement suggestions is generated and saved as `results/recognition_report.md`.", | |
| "category": "Performance Metrics", | |
| "satisfied": null | |
| } | |
| ], | |
| "preferences": [ | |
| { | |
| "preference_id": 0, | |
| "criteria": "The installation process for the \"DeepSpeech\" library should be well-documented, with troubleshooting tips if the library fails to install. Refer to the [DeepSpeech documentation](https://deepspeech.readthedocs.io/en/r0.9/) for guidance.", | |
| "satisfied": null | |
| }, | |
| { | |
| "preference_id": 1, | |
| "criteria": "The visualizations should clearly depict the stages of audio processing, making it easy to interpret the effects of preprocessing.", | |
| "satisfied": null | |
| }, | |
| { | |
| "preference_id": 2, | |
| "criteria": "The report should include recommendations for alternative models or approaches if the \"DeepSpeech\" library proves challenging to implement.", | |
| "satisfied": null | |
| } | |
| ], | |
| "is_kaggle_api_needed": false, | |
| "is_training_needed": true, | |
| "is_web_navigation_needed": true | |
| } |